Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

Steps to be followed:-

• Read the datasets using pandas library

• Clean the data using re library to remove all the external information, to reduce the suffix and prefix I have used nltk.snowball.steamer.

• Used word tokenizer and stopwords(English) to filter the data more.

• Split the dataset into test and train test

• Used count vectorizer and TFIDF to convert the data into vectors and fit them into the sets.

• Used Multinomial Naïve byes theorem to create the model.

• Dumped the model using pipeline

• Create an app using streamlite

Conclusion:

Created an app the app successfully that analyze the given statement and provide the output based on that.

Note-Few features may not work now as I am currently working on updgrading the model and pre-processing part using tweepy api.